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In this paper, we introduce a graph recurrent neural network\n(GRNN) for scalable semi-supervised learning from multi-relational data. Key\naspects of the novel GRNN architecture are the use of multi-relational graphs,\nthe dynamic adaptation to the different relations via learnable weights, and\nthe consideration of graph-based regularizers to promote smoothness and\nalleviate over-parametrization. Our ultimate goal is to design a powerful\nlearning architecture able to: discover complex and highly non-linear data\nassociations, combine (and select) multiple types of relations, and scale\ngracefully with respect to the size of the graph. Numerical tests with real\ndata sets corroborate the design goals and illustrate the performance gains\nrelative to competing alternatives.","url_abs":"http://arxiv.org/abs/1811.02061v3","url_pdf":"http://arxiv.org/pdf/1811.02061v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-recurrent-graph-neural-network-for-multi","repo_url":"https://github.com/bioannidis/adaptive_recurrent_graph_neural_network","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"sociology","task_name":"Sociology"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.02061","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.02061"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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